Descripción

Este es un análisis exploratorio de datos con fines didácticos. El análisis es realizado con datos relacionados a COVID-19 reportados por diversos países. Los datos son obtenidos del sitio: https://ourworldindata.org/coronavirus-source-data. El conjunto de datos del sitio es actualizado constantemente. El resultado del análisis cambia de acuerdo al día en que se ejecuta el código.

Lectura de datos

Se cargan los datos directamente del sitio para obtener la información actualizada al día de ejecución del código.

covid.df <- read.csv("https://covid.ourworldindata.org/data/owid-covid-data.csv")

Se cargan los paquetes necesarios:

library(ggplot2)
library(knitr)
library(reshape2)
library(kableExtra)
#library(dplyr)
library(tidyverse)
library(lubridate)
library(gmodels)
plotscaption <- "oscarcastrolopez.github.io"

La estructura del dataframe con las variables y su tipo:

str(covid.df)

## 'data.frame':    37194 obs. of  36 variables:
##  $ iso_code                       : Factor w/ 212 levels "","ABW","AFG",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ continent                      : Factor w/ 7 levels "","Africa","Asia",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ location                       : Factor w/ 212 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ date                           : Factor w/ 230 levels "2019-12-31","2020-01-01",..: 1 2 3 4 5 6 7 8 9 10 ...
##  $ total_cases                    : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ new_cases                      : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ total_deaths                   : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ new_deaths                     : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ total_cases_per_million        : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ new_cases_per_million          : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ total_deaths_per_million       : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ new_deaths_per_million         : num  0 0 0 0 0 0 0 0 0 0 ...
##  $ new_tests                      : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ total_tests                    : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ total_tests_per_thousand       : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ new_tests_per_thousand         : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ new_tests_smoothed             : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ new_tests_smoothed_per_thousand: num  NA NA NA NA NA NA NA NA NA NA ...
##  $ tests_per_case                 : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ positive_rate                  : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ tests_units                    : Factor w/ 6 levels "","people tested",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ stringency_index               : num  NA 0 0 0 0 0 0 0 0 0 ...
##  $ population                     : num  38928341 38928341 38928341 38928341 38928341 ...
##  $ population_density             : num  54.4 54.4 54.4 54.4 54.4 ...
##  $ median_age                     : num  18.6 18.6 18.6 18.6 18.6 18.6 18.6 18.6 18.6 18.6 ...
##  $ aged_65_older                  : num  2.58 2.58 2.58 2.58 2.58 ...
##  $ aged_70_older                  : num  1.34 1.34 1.34 1.34 1.34 ...
##  $ gdp_per_capita                 : num  1804 1804 1804 1804 1804 ...
##  $ extreme_poverty                : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ cardiovasc_death_rate          : num  597 597 597 597 597 ...
##  $ diabetes_prevalence            : num  9.59 9.59 9.59 9.59 9.59 9.59 9.59 9.59 9.59 9.59 ...
##  $ female_smokers                 : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ male_smokers                   : num  NA NA NA NA NA NA NA NA NA NA ...
##  $ handwashing_facilities         : num  37.7 37.7 37.7 37.7 37.7 ...
##  $ hospital_beds_per_thousand     : num  0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 ...
##  $ life_expectancy                : num  64.8 64.8 64.8 64.8 64.8 ...

La descripción de cada una de las variables de este conjunto de datos se encuentra en el sitio: https://github.com/owid/covid-19-data/blob/master/public/data/owid-covid-codebook.csv.

Ajuste de la columna date que es tipo factor a tipo date:

covid.df$date <- as.Date(covid.df$date, format("%Y-%m-%d"))

Lista de países del conjunto de datos

levels(covid.df$location)

##   [1] "Afghanistan"                      "Albania"                         
##   [3] "Algeria"                          "Andorra"                         
##   [5] "Angola"                           "Anguilla"                        
##   [7] "Antigua and Barbuda"              "Argentina"                       
##   [9] "Armenia"                          "Aruba"                           
##  [11] "Australia"                        "Austria"                         
##  [13] "Azerbaijan"                       "Bahamas"                         
##  [15] "Bahrain"                          "Bangladesh"                      
##  [17] "Barbados"                         "Belarus"                         
##  [19] "Belgium"                          "Belize"                          
##  [21] "Benin"                            "Bermuda"                         
##  [23] "Bhutan"                           "Bolivia"                         
##  [25] "Bonaire Sint Eustatius and Saba"  "Bosnia and Herzegovina"          
##  [27] "Botswana"                         "Brazil"                          
##  [29] "British Virgin Islands"           "Brunei"                          
##  [31] "Bulgaria"                         "Burkina Faso"                    
##  [33] "Burundi"                          "Cambodia"                        
##  [35] "Cameroon"                         "Canada"                          
##  [37] "Cape Verde"                       "Cayman Islands"                  
##  [39] "Central African Republic"         "Chad"                            
##  [41] "Chile"                            "China"                           
##  [43] "Colombia"                         "Comoros"                         
##  [45] "Congo"                            "Costa Rica"                      
##  [47] "Cote d'Ivoire"                    "Croatia"                         
##  [49] "Cuba"                             "Curacao"                         
##  [51] "Cyprus"                           "Czech Republic"                  
##  [53] "Democratic Republic of Congo"     "Denmark"                         
##  [55] "Djibouti"                         "Dominica"                        
##  [57] "Dominican Republic"               "Ecuador"                         
##  [59] "Egypt"                            "El Salvador"                     
##  [61] "Equatorial Guinea"                "Eritrea"                         
##  [63] "Estonia"                          "Ethiopia"                        
##  [65] "Faeroe Islands"                   "Falkland Islands"                
##  [67] "Fiji"                             "Finland"                         
##  [69] "France"                           "French Polynesia"                
##  [71] "Gabon"                            "Gambia"                          
##  [73] "Georgia"                          "Germany"                         
##  [75] "Ghana"                            "Gibraltar"                       
##  [77] "Greece"                           "Greenland"                       
##  [79] "Grenada"                          "Guam"                            
##  [81] "Guatemala"                        "Guernsey"                        
##  [83] "Guinea"                           "Guinea-Bissau"                   
##  [85] "Guyana"                           "Haiti"                           
##  [87] "Honduras"                         "Hong Kong"                       
##  [89] "Hungary"                          "Iceland"                         
##  [91] "India"                            "Indonesia"                       
##  [93] "International"                    "Iran"                            
##  [95] "Iraq"                             "Ireland"                         
##  [97] "Isle of Man"                      "Israel"                          
##  [99] "Italy"                            "Jamaica"                         
## [101] "Japan"                            "Jersey"                          
## [103] "Jordan"                           "Kazakhstan"                      
## [105] "Kenya"                            "Kosovo"                          
## [107] "Kuwait"                           "Kyrgyzstan"                      
## [109] "Laos"                             "Latvia"                          
## [111] "Lebanon"                          "Lesotho"                         
## [113] "Liberia"                          "Libya"                           
## [115] "Liechtenstein"                    "Lithuania"                       
## [117] "Luxembourg"                       "Macedonia"                       
## [119] "Madagascar"                       "Malawi"                          
## [121] "Malaysia"                         "Maldives"                        
## [123] "Mali"                             "Malta"                           
## [125] "Mauritania"                       "Mauritius"                       
## [127] "Mexico"                           "Moldova"                         
## [129] "Monaco"                           "Mongolia"                        
## [131] "Montenegro"                       "Montserrat"                      
## [133] "Morocco"                          "Mozambique"                      
## [135] "Myanmar"                          "Namibia"                         
## [137] "Nepal"                            "Netherlands"                     
## [139] "New Caledonia"                    "New Zealand"                     
## [141] "Nicaragua"                        "Niger"                           
## [143] "Nigeria"                          "Northern Mariana Islands"        
## [145] "Norway"                           "Oman"                            
## [147] "Pakistan"                         "Palestine"                       
## [149] "Panama"                           "Papua New Guinea"                
## [151] "Paraguay"                         "Peru"                            
## [153] "Philippines"                      "Poland"                          
## [155] "Portugal"                         "Puerto Rico"                     
## [157] "Qatar"                            "Romania"                         
## [159] "Russia"                           "Rwanda"                          
## [161] "Saint Kitts and Nevis"            "Saint Lucia"                     
## [163] "Saint Vincent and the Grenadines" "San Marino"                      
## [165] "Sao Tome and Principe"            "Saudi Arabia"                    
## [167] "Senegal"                          "Serbia"                          
## [169] "Seychelles"                       "Sierra Leone"                    
## [171] "Singapore"                        "Sint Maarten (Dutch part)"       
## [173] "Slovakia"                         "Slovenia"                        
## [175] "Somalia"                          "South Africa"                    
## [177] "South Korea"                      "South Sudan"                     
## [179] "Spain"                            "Sri Lanka"                       
## [181] "Sudan"                            "Suriname"                        
## [183] "Swaziland"                        "Sweden"                          
## [185] "Switzerland"                      "Syria"                           
## [187] "Taiwan"                           "Tajikistan"                      
## [189] "Tanzania"                         "Thailand"                        
## [191] "Timor"                            "Togo"                            
## [193] "Trinidad and Tobago"              "Tunisia"                         
## [195] "Turkey"                           "Turks and Caicos Islands"        
## [197] "Uganda"                           "Ukraine"                         
## [199] "United Arab Emirates"             "United Kingdom"                  
## [201] "United States"                    "United States Virgin Islands"    
## [203] "Uruguay"                          "Uzbekistan"                      
## [205] "Vatican"                          "Venezuela"                       
## [207] "Vietnam"                          "Western Sahara"                  
## [209] "World"                            "Yemen"                           
## [211] "Zambia"                           "Zimbabwe"

Continentes del conjunto de datos

levels(covid.df$continent)

## [1] ""              "Africa"        "Asia"          "Europe"       
## [5] "North America" "Oceania"       "South America"

¿Cuál es la cantidad de días máximo que ha sido reportado por los países?

maxdays <- max(table(covid.df$location))
print(paste("Días máximos reportados:",maxdays))

## [1] "Días máximos reportados: 230"

Países que han reportado datos la mayor cantidad de días

names(table(covid.df$location)[table(covid.df$location)==maxdays])

##  [1] "Australia"            "Austria"              "Belarus"             
##  [4] "Belgium"              "Brazil"               "Canada"              
##  [7] "China"                "Croatia"              "Czech Republic"      
## [10] "Denmark"              "Estonia"              "Finland"             
## [13] "France"               "Germany"              "Greece"              
## [16] "Iceland"              "Iran"                 "Israel"              
## [19] "Italy"                "Japan"                "Lithuania"           
## [22] "Luxembourg"           "Malaysia"             "Mexico"              
## [25] "Nepal"                "Netherlands"          "Norway"              
## [28] "Russia"               "Singapore"            "South Korea"         
## [31] "Sweden"               "Switzerland"          "Taiwan"              
## [34] "Thailand"             "United Arab Emirates" "United Kingdom"      
## [37] "United States"        "Vietnam"              "World"

¿En qué fechas inician y terminan los datos reportados?

startdate <- min(covid.df$date)
enddate <- max(covid.df$date)
todayformatted <- format(Sys.Date(), "%A, %d de %B de %Y")
todayformatted <- paste(toupper(substr(todayformatted, 1, 1)), 
                        substr(todayformatted, 2, nchar(todayformatted)), 
                        sep="")
print(paste("Inicia el", format(startdate, "%d de %B de %Y"), 
            "y termina el",  format(enddate, "%d de %B de %Y")))

## [1] "Inicia el 31 de diciembre de 2019 y termina el 16 de agosto de 2020"

Rankings de países con las variables de totales o acumulados

Del dataframe original, se obtienen sólo las observaciones en donde la columna date sea igual a la fecha máxima. De esta manera se obtienen los totales o acumulados actualizados de cada país. Adicionalmente, se eliminan los países con menos de 1 millón de habitantes. Algunos países con poca población tienen estadísticas por miilón de habitantes muy altas.

Con las siguientes variables se crean los rankings:

# Get one row of each country with the updated totals
covid.total.df <- covid.df[covid.df$date== enddate,]
# Filter countries with less than 1 millón population
covid.total.df <- covid.total.df[covid.total.df$population >= 1000000,]
covid.total.df$location <- droplevels.factor(covid.total.df$location)
print(names(covid.total.df)[c(5,7,9,11)])

## [1] "total_cases"              "total_deaths"            
## [3] "total_cases_per_million"  "total_deaths_per_million"

Al descartar los países que tienen al menos 1 millón de habitantes, el conjunto de datos ahora tiene: 156 Países.

Para cada una de las variables con totales, se obtienen los 20 países con el valor más alto y los 20 países con el valor más bajo. En caso de que México no se encuentre en los primeros 20 países, se agrega en la lista indicando la posición que le toca tomando en cuenta todos los países. Para cada variable a analizar se hace lo siguiente en R:

Se ordenan los datos con la columna correspondiente y se guarda en un nuevo dataframe.
Se guardan sólo los 20 países con los valores más altos y los 20 países con valores más bajos.
Se filtran las cólumnas de interés, el resto de columnas se descarta. Adicionalmente, se agrega una nueva columna indicando con un valor numérico la posición en la que se encuentra el país en el ranking.
Se muestran los datos en una tabla y una gráfica de barras horizontales.

Ranking de países: Total de casos reportados de COVID-19 con la columna total_cases

# Data is ordered according to the total_cases column
ranking.total_cases <- covid.total.df[order(-covid.total.df$total_cases),]
# The row corresponding to World is removed
ranking.total_cases <- ranking.total_cases[ranking.total_cases$location != "World", ]
# A new column indicating the positionin the rank is added
ranking.total_cases$position <- 1:nrow(ranking.total_cases)
# Only columns of interest are kept
columnfilter <- c("position", "location", "total_cases")
bottom20.total_cases <- tail(ranking.total_cases[, columnfilter],20)
top20.total_cases <- head(ranking.total_cases[, columnfilter],20)
rm(ranking.total_cases)
rownames(top20.total_cases) <- c()
rownames(bottom20.total_cases) <- c()

mexrow <- which(top20.total_cases$location=='Mexico')
top20.total_cases$total_cases_formated <- formatC(top20.total_cases$total_cases, 
                                                  format="f", big.mark=",", digits=0)
bottom20.total_cases$total_cases_formated <- formatC(bottom20.total_cases$total_cases, 
                                                     format="f", big.mark=",", digits=0)

Tablas

tablecolnames <- c("Posición", "País", "Casos totales")
kable(top20.total_cases[,c(1,2,4)], format = "html", align = "clr", 
      col.names=tablecolnames, caption="Países con más casos totales") %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), 
                full_width = F, position = "float_left") %>%
  row_spec(mexrow, bold = T, color = "black", background = "yellow")

kable(bottom20.total_cases[,c(1,2,4)], format = "html", align = "clr", 
      col.names=tablecolnames, caption="Países con menos casos totales") %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), 
                full_width = F, position = "right")

Países con más casos totales
Posición	País	Casos totales
1	United States	5,361,165
2	Brazil	3,317,096
3	India	2,589,682
4	Russia	917,884
5	South Africa	583,653
6	Peru	525,803
7	Mexico	517,714
8	Colombia	456,689
9	Chile	383,902
10	Iran	341,070
11	United Kingdom	316,367
12	Saudi Arabia	297,315
13	Pakistan	288,717
14	Argentina	282,424
15	Bangladesh	274,525
16	Italy	253,438
17	Turkey	248,117
18	Germany	223,453
19	France	215,521
20	Iraq	172,583

Países con menos casos totales
Posición	País	Casos totales
136	Burkina Faso	1,249
137	Botswana	1,214
138	Niger	1,165
139	Togo	1,130
140	Jamaica	1,082
141	Chad	952
142	Vietnam	934
143	Lesotho	903
144	Tanzania	509
145	Taiwan	484
146	Trinidad and Tobago	474
147	Burundi	412
148	Myanmar	374
149	Mauritius	344
150	Mongolia	298
151	Eritrea	285
152	Cambodia	273
153	Papua New Guinea	271
154	Timor	25
155	Laos	20

Gráfica top 20

ggplot(data=top20.total_cases, aes(x=reorder(paste(position, location),total_cases), 
                                   y=total_cases, fill=location))+
  geom_bar(stat = "identity", position=position_dodge(), colour="black", show.legend = FALSE)+
  ylab("Casos totales de COVID-19") +
  geom_text(aes(y=max(total_cases)+170000, 
                label=total_cases_formated,
                fontface="bold"), 
            color="black")+
  labs(title="Top 20 de los países con más casos de COVID-19", 
       subtitle = todayformatted,
       caption = plotscaption)+
  scale_y_continuous(breaks=c(100000, 500000, 750000, 1000000, 2000000, 3000000, 4000000, 5000000),
                     label=c("100k", "500k", "750k", "1m", "2m", "3m", "4m", "5m"))+
  coord_flip() +
  xlab("Países") +
  theme_bw()+
  theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"))

Ranking de países: Total de casos reportados de COVID-19 con la columna total_cases - Países de América

Se obtiene el ranking sólo incluyendo países del continente americano. Se obtienen los datos en donde la columna continent sea igual a “North America” o “South America”.

ranking.total_cases.america <- covid.total.df[covid.total.df$continent %in% 
                                                c("North America", "South America") ,]
ranking.total_cases.america <- ranking.total_cases.america[order(-ranking.total_cases.america$total_cases),]
ranking.total_cases.america$position <- 1:nrow(ranking.total_cases.america)
bottom20.total_cases.america <- tail(ranking.total_cases.america[, c("position", "location", "total_cases")],5)
top20.total_cases.america <- head(ranking.total_cases.america[, c("position", "location", "total_cases")],20)
rownames(bottom20.total_cases.america) <- c()
rownames(top20.total_cases.america) <- c()
mexrow <- which(ranking.total_cases.america$location=='Mexico')
rm(ranking.total_cases.america)
top20.total_cases.america$total_cases_formated <- formatC(top20.total_cases.america$total_cases,
                                                          format="f", big.mark=",", digits=0)
bottom20.total_cases.america$total_cases_formated <- formatC(bottom20.total_cases.america$total_cases, 
                                                             format="f", big.mark=",", digits=0)

Tabla

tablecolnames <- c("Posición", "País", "Casos totales")
kable(top20.total_cases.america[,c(1,2,4)], format = "html", align = "clr", 
      col.names=tablecolnames, caption="Países del continente Americano con más casos totales") %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), 
                full_width = F, position = "center") %>%
row_spec(mexrow, bold = T, color = "black", background = "yellow")

Países del continente Americano con más casos totales
Posición	País	Casos totales
1	United States	5,361,165
2	Brazil	3,317,096
3	Peru	525,803
4	Mexico	517,714
5	Colombia	456,689
6	Chile	383,902
7	Argentina	282,424
8	Canada	121,889
9	Ecuador	100,688
10	Bolivia	99,146
11	Dominican Republic	85,545
12	Panama	80,665
13	Guatemala	62,313
14	Honduras	49,979
15	Venezuela	32,607
16	Costa Rica	27,737
17	Puerto Rico	25,695
18	El Salvador	22,619
19	Paraguay	9,381
20	Haiti	7,831

Gráfica top 20

ggplot(data=top20.total_cases.america, aes(x=reorder(paste(position, location),total_cases),
                                           y=total_cases, fill=location))+
  geom_bar(stat = "identity", position=position_dodge(), colour="black", show.legend = FALSE)+
  ylab("Casos de COVID-19") +
  geom_text(aes(y=max(total_cases)+170000, 
                label=format(total_cases, big.mark=","),
                fontface="bold"), 
            color="black")+
  labs(title="Top 20 de los países con más casos de COVID-19 de América", 
       subtitle = todayformatted,
       caption = plotscaption)+
  scale_y_continuous(breaks=c(100000, 500000, 750000, 1000000, 2000000, 3000000, 4000000, 5000000),
                     label=c("100k", "500k", "750k", "1m", "2m", "3m", "4m", "5m"))+
  coord_flip() +
  xlab("Países") +
  theme_bw()+
  theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"))

Ranking de países: Total de casos reportados de COVID-19 por millón de habitantes con la columna total_cases_per_million

ranking.total_cases_per_million <- covid.total.df[order(-covid.total.df$total_cases_per_million),]
ranking.total_cases_per_million$position <- 1:nrow(ranking.total_cases_per_million)
columnfilter <- c("position", "location", "total_cases_per_million")
mexico.total_cases_per_million <- ranking.total_cases_per_million[
                                  ranking.total_cases_per_million$location == "Mexico", ]
bottom20.total_cases_per_million <- tail(ranking.total_cases_per_million[, columnfilter],20)
top20.total_cases_per_million <- head(ranking.total_cases_per_million[, columnfilter],20)
mexico.total_cases_per_million <- mexico.total_cases_per_million[, columnfilter]
rm(ranking.total_cases_per_million)
rownames(top20.total_cases_per_million) <- c()
rownames(bottom20.total_cases_per_million) <- c()
rownames(mexico.total_cases_per_million) <- c()

top20.total_cases_per_million <- rbind(top20.total_cases_per_million, mexico.total_cases_per_million)
mexrow <- which(top20.total_cases_per_million$location=='Mexico')

top20.total_cases_per_million$total_cases_per_million_formated <- formatC(
  top20.total_cases_per_million$total_cases_per_million, format="f", big.mark=",", digits=2)
bottom20.total_cases_per_million$total_cases_per_million_formated<- formatC(
  bottom20.total_cases_per_million$total_cases_per_million, format="f", big.mark=",", digits=2)

Tablas

tablecolnames <- c("Posición", "País", "Casos por mdh")
kable(top20.total_cases_per_million[,c(1,2,4)], format = "html", align = "clr", 
      col.names=tablecolnames, caption="Países con más casos por millón de habitantes (mdh)") %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), 
                full_width = F, position = "float_left") %>%
  row_spec(mexrow, bold = T, color = "black", background = "yellow")

kable(bottom20.total_cases_per_million[,c(1,2,4)], format = "html", align = "clr", 
      col.names=tablecolnames, caption="Países con menos casos por millón de habitantes (mdh)") %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), 
                full_width = F, position = "right")

Países con más casos por millón de habitantes (mdh)
Posición	País	Casos por mdh
1	Qatar	39,849.57
2	Bahrain	27,286.36
3	Chile	20,082.54
4	Panama	18,695.10
5	Kuwait	17,725.30
6	Oman	16,238.52
7	United States	16,196.74
8	Peru	15,947.03
9	Brazil	15,605.50
10	Armenia	14,003.28
11	Israel	10,668.66
12	South Africa	9,840.94
13	Singapore	9,514.14
14	Puerto Rico	8,981.63
15	Colombia	8,975.30
16	Saudi Arabia	8,540.13
17	Bolivia	8,493.60
18	Sweden	8,346.54
19	Dominican Republic	7,885.85
20	Moldova	7,413.31
43	Mexico	4,015.38

Países con menos casos por millón de habitantes (mdh)
Posición	País	Casos por mdh
137	Mongolia	90.90
138	Mozambique	89.30
139	Eritrea	80.36
140	Yemen	62.43
141	China	62.03
142	Burkina Faso	59.75
143	Chad	57.96
144	Angola	56.35
145	Thailand	48.38
146	Niger	48.13
147	Burundi	34.65
148	Uganda	31.35
149	Papua New Guinea	30.29
150	Taiwan	20.32
151	Timor	18.96
152	Cambodia	16.33
153	Vietnam	9.60
154	Tanzania	8.52
155	Myanmar	6.87
156	Laos	2.75

Gráfica top 20

ggplot(data=top20.total_cases_per_million, 
       aes(x=reorder(paste(position, location),total_cases_per_million), 
           y=total_cases_per_million, fill=location))+
  geom_bar(stat = "identity", position=position_dodge(), colour="black", show.legend = FALSE)+
  ylab("Casos de COVID-19 por millón de habitantes") +
  geom_text(aes(y=max(total_cases_per_million)+1250, 
                label=total_cases_per_million_formated,
                fontface="bold"), 
            color="black")+
  labs(title="Top 20 de los países con más casos reportados de COVID-19 por millón de habitantes (+ México)",
       subtitle = todayformatted,
       caption = plotscaption)+
  coord_flip() +
  xlab("Países") +
  theme_bw()+
  theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"))

Ranking de países: Total de muertes reportadas por COVID-19 con la columna total_deaths

ranking.total_deaths <- covid.total.df[order(-covid.total.df$total_deaths),]
ranking.total_deaths <- ranking.total_deaths[ranking.total_deaths$location != "World", ]
ranking.total_deaths$position <- 1:nrow(ranking.total_deaths)
columnfilter <- c("position", "location", "total_deaths")
bottom20.total_deaths <- tail(ranking.total_deaths[, columnfilter],20)
top20.total_deaths <- head(ranking.total_deaths[, columnfilter],20)
mexrow <- which(ranking.total_deaths$location=='Mexico')
rm(ranking.total_deaths)
rownames(top20.total_deaths) <- c()
rownames(bottom20.total_deaths) <- c()

top20.total_deaths$total_deaths_formated <- formatC(top20.total_deaths$total_deaths, 
                                                  format="f", big.mark=",", digits=0)
bottom20.total_deaths$total_deaths_formated <- formatC(bottom20.total_deaths$total_deaths, 
                                                     format="f", big.mark=",", digits=0)

Tablas

tablecolnames <- c("Posición", "País", "Muertes totales")
kable(top20.total_deaths[,c(1,2,4)], format = "html", align = "clr", 
      col.names=tablecolnames, caption="Países con más muertes por COVID-19") %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), 
                full_width = F, position = "float_left") %>%
  row_spec(mexrow, bold = T, color = "black", background = "yellow")

kable(bottom20.total_deaths[,c(1,2,4)], format = "html", align = "clr", 
      col.names=tablecolnames, caption="Países con menos muertes por COVID-19") %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), 
                full_width = F, position = "right")

Países con más muertes por COVID-19
Posición	País	Muertes totales
1	United States	169,481
2	Brazil	107,232
3	Mexico	56,543
4	India	49,980
5	United Kingdom	41,358
6	Italy	35,392
7	France	30,409
8	Peru	26,075
9	Iran	19,492
10	Russia	15,617
11	Colombia	14,810
12	South Africa	11,677
13	Chile	10,395
14	Belgium	9,935
15	Germany	9,231
16	Canada	9,024
17	Pakistan	6,168
18	Netherlands	6,160
19	Indonesia	6,071
20	Ecuador	6,065

Países con menos muertes por COVID-19
Posición	País	Muertes totales
136	Tanzania	21
137	Mozambique	19
138	Georgia	17
139	Jamaica	14
140	Uganda	13
141	Jordan	11
142	Sri Lanka	11
143	Mauritius	10
144	Trinidad and Tobago	10
145	Rwanda	8
146	Taiwan	7
147	Myanmar	6
148	Botswana	3
149	Papua New Guinea	3
150	Burundi	1
151	Cambodia	0
152	Eritrea	0
153	Laos	0
154	Mongolia	0
155	Timor	0

Gráfica top 20

ggplot(data=top20.total_deaths, 
       aes(x=reorder(paste(position, location),total_deaths), 
                                   y=total_deaths, fill=location))+
  geom_bar(stat = "identity", position=position_dodge(), colour="black", show.legend = FALSE)+
  ylab("Muertes por COVID-19") +
  geom_text(aes(y=max(total_deaths)+7500, 
                label=total_deaths_formated,
                fontface="bold"), 
            color="black")+
  labs(title="Top 20 de los países con más muertes por COVID-19",
       subtitle = todayformatted,
       caption = plotscaption)+
  scale_y_continuous(breaks=c(10000, 25000, 50000, 75000, 100000, 150000, 170000), 
                     label=c("10k", "25k", "50k", "75k", "100k", "150k", "170k"))+
  coord_flip() +
  xlab("Países") +
  theme_bw()+
  theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"))

Ranking de países: Total de muertes reportadas por COVID-19 por millón de habitantes con la columna total_deaths_per_million

ranking.total_deaths_per_million <- covid.total.df[order(-covid.total.df$total_deaths_per_million),]
ranking.total_deaths_per_million$position <- 1:nrow(ranking.total_deaths_per_million)
columnfilter <- c("position", "location", "total_deaths_per_million")
bottom20.total_deaths_per_million <- tail(ranking.total_deaths_per_million[, columnfilter],20)
top20.total_deaths_per_million <- head(ranking.total_deaths_per_million[, columnfilter],20)
mexrow <- which(ranking.total_deaths_per_million$location=='Mexico')
rm(ranking.total_deaths_per_million)
rownames(top20.total_deaths_per_million) <- c()
rownames(bottom20.total_deaths_per_million) <- c()

top20.total_deaths_per_million$total_deaths_per_million_formated <- formatC(
  top20.total_deaths_per_million$total_deaths_per_million, format="f", big.mark=",", digits=2)
bottom20.total_deaths_per_million$total_deaths_per_million_formated <- formatC(
  bottom20.total_deaths_per_million$total_deaths_per_million, format="f", big.mark=",", digits=2)

Tabla

tablecolnames <- c("Posición", "País", "Muertes por mdh")

kable(top20.total_deaths_per_million[,c(1,2,4)], format = "html", align = "clr", 
      col.names=tablecolnames, 
      caption="Países con más muertes por COVID-19 por millón de habitantes (mdh)") %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), 
                full_width = F, position = "float_left") %>%
  row_spec(mexrow, bold = T, color = "black", background = "yellow")

kable(bottom20.total_deaths_per_million[,c(1,2,4)], format = "html", align = "clr", 
      col.names=tablecolnames, 
      caption="Países con menos muertes por COVID-19 por millón de habitantes (mdh)") %>% 
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"), 
                full_width = F, position = "right")

Países con más muertes por COVID-19 por millón de habitantes (mdh)
Posición	País	Muertes por mdh
1	Belgium	857.23
2	Peru	790.83
3	United Kingdom	609.23
4	Italy	585.36
5	Sweden	572.62
6	Chile	543.78
7	United States	512.02
8	Brazil	504.48
9	France	465.87
10	Mexico	438.55
11	Panama	404.66
12	Netherlands	359.50
13	Ireland	359.27
14	Ecuador	343.76
15	Bolivia	342.93
16	Colombia	291.06
17	Armenia	275.71
18	Macedonia	258.71
19	Canada	239.10
20	Iran	232.07

Países con menos muertes por COVID-19 por millón de habitantes (mdh)
Posición	País	Muertes por mdh
137	Angola	2.62
138	Burkina Faso	2.58
139	Botswana	1.28
140	Jordan	1.08
141	Thailand	0.83
142	Rwanda	0.62
143	Mozambique	0.61
144	Sri Lanka	0.51
145	Tanzania	0.35
146	Papua New Guinea	0.34
147	Taiwan	0.29
148	Uganda	0.28
149	Vietnam	0.23
150	Myanmar	0.11
151	Burundi	0.08
152	Cambodia	0.00
153	Eritrea	0.00
154	Laos	0.00
155	Mongolia	0.00
156	Timor	0.00

Gráfica top 20

ggplot(data=top20.total_deaths_per_million, 
       aes(x=reorder(paste(position, location),total_deaths_per_million), 
           y=total_deaths_per_million, fill=location))+
  geom_bar(stat = "identity", position=position_dodge(), colour="black", show.legend = FALSE)+
  ylab("Muertes por COVID-19 por millón de habitantes") +
  geom_text(aes(y=max(total_deaths_per_million)+25, 
                label=total_deaths_per_million_formated,
                fontface="bold"),
            color="black")+
  labs(title="Top 20 de los países con más muertes por COVID-19 por millón de habitantes",
       subtitle = todayformatted,
       caption = plotscaption)+
  coord_flip() +
  xlab("Países") +
  theme_bw()+
  theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"))

Tendencias de contagios y muertes utilizando las columnas: new_cases y new_deaths

Nuevos contagios de los 5 países con más casos reportados de COVID-19 + México

Las siguientes funciones son para facilitar la creación de las gráficas de líneas.

##Line plot of new_cases with date breaks by month
plot.trend.new_cases.month <- function(startdate, enddate, countries, graphtitle, graphsubtitle){
  tmp.df <- covid.df[covid.df$location %in% countries,]
  tmp.df <- tmp.df[tmp.df$date >= startdate,]
  tmp.df <- tmp.df[tmp.df$date <= enddate,]
  ggplot(data=tmp.df , aes(x=date, y=new_cases, group=location, colour=location)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Países")+
    scale_x_date(date_breaks = "month", date_labels = "%B")+
    ggtitle(graphtitle,
            subtitle = graphsubtitle)+
    labs(caption = plotscaption)+
    ylab("Nuevos casos de COVID-19")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
          axis.text.y = element_text(size=11, face="bold", colour = "black"), 
          axis.text.x = element_text(size=11, face="bold", colour = "black"),
          legend.position = "bottom",
          legend.title = element_text(size = 14),
          legend.text = element_text(size = 13),
          legend.key.width = unit(1.5,"cm"))+
    guides(colour = guide_legend(override.aes = list(size=2)))
}
##Line plot of new_cases with date breaks by short month and days
plot.trend.new_cases.monthday <- function(startdate, enddate, countries, graphtitle, graphsubtitle){
  tmp.df <- covid.df[covid.df$location %in% countries,]
  tmp.df <- tmp.df[tmp.df$date >= startdate,]
  tmp.df <- tmp.df[tmp.df$date <= enddate,]
  ggplot(data=tmp.df , aes(x=date, y=new_cases, group=location, colour=location)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Países")+
    scale_x_date(date_breaks = "day", date_labels = "%b %d")+
    ggtitle(graphtitle,
            subtitle = graphsubtitle)+
    labs(caption = plotscaption)+
    ylab("Nuevos casos de COVID-19")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
          axis.text.y = element_text(size=11, face="bold", colour = "black"), 
          axis.text.x = element_text(size=11, face="bold", colour = "black"),
          legend.position = "bottom",
          legend.title = element_text(size = 14),
          legend.text = element_text(size = 13),
          legend.key.width = unit(1.5,"cm"))+
    guides(colour = guide_legend(override.aes = list(size=2)))
}

Gráficas con rangos de fechas. Las fechas iniciales son distintas, la fecha final siempre es el día Domingo, 16 de agosto de 2020.

Todo

top5.total_cases <- c(head(as.character(top20.total_cases$location),5), "Mexico")
sublabel.tmp <- paste("Rango:", format(startdate, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.new_cases.month(startdate, enddate, top5.total_cases, "Tendencia de contagios de COVID-19", sublabel.tmp)

-5 Meses

mes.tmp <- as.numeric(format(enddate, "%m"))-5
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.new_cases.month(startdate.tmp, enddate, top5.total_cases, "Tendencia de contagios de COVID-19", sublabel.tmp)

-3 Meses

mes.tmp <- as.numeric(format(enddate, "%m"))-3
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.new_cases.month(startdate.tmp, enddate, top5.total_cases, "Tendencia de contagios de COVID-19", sublabel.tmp)

-2 Meses

mes.tmp <- as.numeric(format(enddate, "%m"))-2
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.new_cases.month(startdate.tmp, enddate, top5.total_cases, "Tendencia de contagios de COVID-19", sublabel.tmp)

-1 Mes

mes.tmp <- as.numeric(format(enddate, "%m"))-1
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.new_cases.month(startdate.tmp, enddate, top5.total_cases, "Tendencia de contagios de COVID-19", sublabel.tmp)

-3 Semanas

sublabel.tmp <- paste("Rango:", format(enddate-21, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.new_cases.monthday(enddate-21, enddate, top5.total_cases, "Tendencia de contagios de COVID-19", sublabel.tmp)

-2 Semanas

sublabel.tmp <- paste("Rango:", format(enddate-14, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.new_cases.monthday(enddate-14, enddate, top5.total_cases, "Tendencia de contagios de COVID-19", sublabel.tmp)

Nuevas muertes de los 5 países con más casos reportados de COVID-19 + México

Funciones para generar las gráficas.

##Line plot of new_deaths with date breaks by month
plot.trend.new_deaths.month <- function(startdate, enddate, countries, graphtitle, graphsubtitle){
  tmp.df <- covid.df[covid.df$location %in% countries,]
  tmp.df <- tmp.df[tmp.df$date >= startdate,]
  tmp.df <- tmp.df[tmp.df$date <= enddate,]
  ggplot(data=tmp.df , aes(x=date, y=new_deaths, group=location, colour=location)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Países")+
    scale_x_date(date_breaks = "month", date_labels = "%B")+
    ggtitle(graphtitle,
            subtitle = graphsubtitle)+
    labs(caption = plotscaption)+
    ylab("Nuevos muertes atribuibles a COVID-19")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
          axis.text.y = element_text(size=11, face="bold", colour = "black"), 
          axis.text.x = element_text(size=11, face="bold", colour = "black"),
          legend.position = "bottom",
          legend.title = element_text(size = 14),
          legend.text = element_text(size = 13),
          legend.key.width = unit(1.5,"cm"))+
    guides(colour = guide_legend(override.aes = list(size=2)))
}
##Line plot of new_deaths with date breaks by short month and day
plot.trend.new_deaths.monthdays <- function(startdate, enddate, countries, graphtitle, graphsubtitle){
  tmp.df <- covid.df[covid.df$location %in% countries,]
  tmp.df <- tmp.df[tmp.df$date >= startdate,]
  tmp.df <- tmp.df[tmp.df$date <= enddate,]
  ggplot(data=tmp.df , aes(x=date, y=new_deaths, group=location, colour=location)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Países")+
    scale_x_date(date_breaks = "day", date_labels = "%b %d")+
    ggtitle(graphtitle,
            subtitle = graphsubtitle)+
    labs(caption = plotscaption)+
    ylab("Nuevos casos de COVID-19")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"),
        legend.position = "bottom",
        legend.title = element_text(size = 14),
        legend.text = element_text(size = 13),
        legend.key.width = unit(1.5,"cm"))+
        guides(colour = guide_legend(override.aes = list(size=2)))
}

Gráficas con rangos de fechas. Las fechas iniciales son distitnas, fecha final siempre es el día Domingo, 16 de agosto de 2020.

Todo

top5.total_cases <- c(head(as.character(top20.total_cases$location),5), "Mexico")
sublabel.tmp <- paste("Rango:", format(startdate, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
title.tmp <- "Tendencia de nuevas muertes por COVID-19"
plot.trend.new_deaths.month(startdate, enddate, top5.total_cases, title.tmp, sublabel.tmp)

-5 Meses

mes.tmp <- as.numeric(format(enddate, "%m"))-5
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.new_deaths.month(startdate.tmp, enddate, top5.total_cases, title.tmp, sublabel.tmp)

-3 Meses

mes.tmp <- as.numeric(format(enddate, "%m"))-3
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.new_deaths.month(startdate.tmp, enddate, top5.total_cases, title.tmp, sublabel.tmp)

-2 Meses

mes.tmp <- as.numeric(format(enddate, "%m"))-2
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.new_deaths.month(startdate.tmp, enddate, top5.total_cases, title.tmp, sublabel.tmp)

-1 Mes

mes.tmp <- as.numeric(format(enddate, "%m"))-1
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.new_deaths.month(startdate.tmp, enddate, top5.total_cases, title.tmp, sublabel.tmp)

-3 Semanas

sublabel.tmp <- paste("Rango:", format(enddate-21, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.new_deaths.monthdays(enddate-21, enddate, top5.total_cases, title.tmp, sublabel.tmp)

-2 Semanas

sublabel.tmp <- paste("Rango:", format(enddate-14, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.new_deaths.monthdays(enddate-14, enddate, top5.total_cases, title.tmp, sublabel.tmp)

Acumulados de casos y muertes COVID-19

Para las siguientes gráficas se utilizan las columnas total_cases y total_deaths.

Curva de contagios acumulados de los 5 países con más casos reportados (+ México)

Gráficas con rangos de fechas. Las fechas iniciales son distitnas, fecha final siempre es el día Domingo, 16 de agosto de 2020.

Funciones para generar las gráficas.

##Line plot of new_total_cases with date breaks by month
plot.trend.total_cases.month <- function(startdate, enddate, countries, graphtitle, graphsubtitle){
  tmp.df <- covid.df[covid.df$location %in% countries,]
  tmp.df <- tmp.df[tmp.df$date >= startdate,]
  tmp.df <- tmp.df[tmp.df$date <= enddate,]
  ggplot(data=tmp.df , aes(x=date, y=total_cases, group=location, colour=location)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Países")+
    scale_x_date(date_breaks = "month", date_labels = "%B")+
    ggtitle(graphtitle,
            subtitle = graphsubtitle)+
    labs(caption = plotscaption)+
    ylab("Casos acumulados de COVID-19")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"),
        legend.position = "bottom",
        legend.title = element_text(size = 14),
        legend.text = element_text(size = 13),
        legend.key.width = unit(1.5,"cm"))+
        guides(colour = guide_legend(override.aes = list(size=2)))
}
##Line plot of new_deaths with date breaks by short month and day
plot.trend.total_cases.monthdays <- function(startdate, enddate, countries, graphtitle, graphsubtitle){
  tmp.df <- covid.df[covid.df$location %in% countries,]
  tmp.df <- tmp.df[tmp.df$date >= startdate,]
  tmp.df <- tmp.df[tmp.df$date <= enddate,]
  ggplot(data=tmp.df , aes(x=date, y=total_cases, group=location, colour=location)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Países")+
    scale_x_date(date_breaks = "day", date_labels = "%b %d")+
    ggtitle(graphtitle,
            subtitle = graphsubtitle)+
    labs(caption = plotscaption)+
    ylab("Casos acumulados de COVID-19")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"),
        legend.position = "bottom",
        legend.title = element_text(size = 14),
        legend.text = element_text(size = 13),
        legend.key.width = unit(1.5,"cm"))+
        guides(colour = guide_legend(override.aes = list(size=2)))
}

Todo

sublabel.tmp <- paste("Rango:", format(startdate, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
title.tmp <- "Contagios acumulados de COVID-19"
plot.trend.total_cases.month(startdate, enddate, top5.total_cases, title.tmp, sublabel.tmp)

-5 Meses

mes.tmp <- as.numeric(format(enddate, "%m"))-5
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.total_cases.month(startdate.tmp, enddate, top5.total_cases, title.tmp, sublabel.tmp)

-3 Meses

mes.tmp <- as.numeric(format(enddate, "%m"))-3
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.total_cases.month(startdate.tmp, enddate, top5.total_cases, title.tmp, sublabel.tmp)

-2 Meses

mes.tmp <- as.numeric(format(enddate, "%m"))-2
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.total_cases.month(startdate.tmp, enddate, top5.total_cases, title.tmp, sublabel.tmp)

-1 Mes

mes.tmp <- as.numeric(format(enddate, "%m"))-1
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.total_cases.month(startdate.tmp, enddate, top5.total_cases, title.tmp, sublabel.tmp)

-1 Mes TOP[3:7]

La cantidad de contagios que tienen Estados Unidos y Brasil no permiten apreciar las curvas del resto de los países. Se eliminan esos 2 países y se agregan los 2 países que se encuentran en la posición 6 y 7 del ranking de los países con más contagios reportados.

mes.tmp <- as.numeric(format(enddate, "%m"))-1
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
total_cases.others <- as.character(top20.total_cases$location)[3:7]
plot.trend.total_cases.month(startdate.tmp, enddate, total_cases.others, title.tmp, sublabel.tmp)

-3 Semanas TOP[3:7]

sublabel.tmp <- paste("Rango:", format(enddate-21, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.total_cases.monthdays(enddate-21, enddate, total_cases.others, title.tmp, sublabel.tmp)

-2 Semanas TOP[3:7]

sublabel.tmp <- paste("Rango:", format(enddate-14, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.total_cases.monthdays(enddate-14, enddate, total_cases.others, title.tmp, sublabel.tmp)

-2 Semanas MX, PE y SA

total_cases.others <- as.character(top20.total_cases$location)[3:7]
total_cases.others <- total_cases.others[c(-1,-2)]
countrieslabels <- paste(total_cases.others, collapse = ', ')
sublabel.tmp <- paste("Rango:", format(enddate-14, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
title.tmp <- paste(title.tmp, "de", countrieslabels)
plot.trend.total_cases.monthdays(enddate-14, enddate, total_cases.others, title.tmp, sublabel.tmp)

Curvas de muertes acumuladas de los 5 países con más muertes reportadas (+ México)

Gráficas con rangos de fechas. Las fechas iniciales son distitnas, fecha final siempre es el día Domingo, 16 de agosto de 2020.

Funciones para generar las gráficas.

# Generate total_deaths line plot with month breaks
plot.trend.total_deaths.month <- function(startdate, enddate, countries, graphtitle, subgraphtitle){
  tmp.df <- covid.df[covid.df$location %in% countries,]
  tmp.df <- tmp.df[tmp.df$date >= startdate,]
  tmp.df <- tmp.df[tmp.df$date <= enddate,]
  ggplot(data=tmp.df , aes(x=date, y=total_deaths, group=location, colour=location)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Países")+
    scale_x_date(date_breaks = "month", date_labels = "%B")+
    ggtitle(graphtitle,
            subtitle = subgraphtitle)+
    labs(caption = plotscaption)+
    ylab("Muertes totales por COVID-19")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"),
        legend.position = "bottom",
        legend.title = element_text(size = 14),
        legend.text = element_text(size = 13),
        legend.key.width = unit(1.5,"cm"))+
        guides(colour = guide_legend(override.aes = list(size=2)))
}
# Generate total_deaths line plot with month and day breaks
plot.trend.total_deaths.monthdays <- function(startdate, enddate, countries, graphtitle, subgraphtitle){
  tmp.df <- covid.df[covid.df$location %in% countries,]
  tmp.df <- tmp.df[tmp.df$date >= startdate,]
  tmp.df <- tmp.df[tmp.df$date <= enddate,]
  ggplot(data=tmp.df , aes(x=date, y=total_deaths, group=location, colour=location)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Países")+
    scale_x_date(date_breaks = "day", date_labels = "%b %d")+
    ggtitle(graphtitle,
            subtitle = subgraphtitle)+
    labs(caption = plotscaption)+
    ylab("Muertes totales por COVID-19")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"),
        legend.position = "bottom",
        legend.title = element_text(size = 14),
        legend.text = element_text(size = 13),
        legend.key.width = unit(1.5,"cm"))+
        guides(colour = guide_legend(override.aes = list(size=2)))
}

Todo

top5.total_deaths <-  head(as.character(top20.total_deaths$location),5)
sublabel.tmp <- paste("Rango:", format(startdate, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
title.tmp <- "Muertes acumuladas por COVID-19"
plot.trend.total_deaths.month(startdate, enddate, top5.total_deaths, title.tmp, sublabel.tmp)

-5 Meses

mes.tmp <- as.numeric(format(enddate, "%m"))-5
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.total_deaths.month(startdate.tmp, enddate, top5.total_deaths, title.tmp, sublabel.tmp)

-3 Meses

mes.tmp <- as.numeric(format(enddate, "%m"))-3
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.total_deaths.month(startdate.tmp, enddate, top5.total_deaths, title.tmp, sublabel.tmp)

-2 Meses

mes.tmp <- as.numeric(format(enddate, "%m"))-2
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.total_deaths.month(startdate.tmp, enddate, top5.total_deaths, title.tmp, sublabel.tmp)

-1 Meses

mes.tmp <- as.numeric(format(enddate, "%m"))-1
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.total_deaths.month(startdate.tmp, enddate, top5.total_deaths, title.tmp, sublabel.tmp)

-1 Mes TOP[3:7]

La cantidad de muertes que tienen Estados Unidos y Brasil no permiten apreciar las curvas del resto de los países. Se quita a esos 2 países y se agrega a los 2 países que sigan en el ranking de países con más muertes totales.

mes.tmp <- as.numeric(format(enddate, "%m"))-1
mes.tmp <- ifelse(mes.tmp < 10, paste0("0",mes.tmp), mes.tmp)
startdate.tmp <- paste0("2020-", mes.tmp, "-01")
startdate.tmp <- as.Date(startdate.tmp)
sublabel.tmp <- paste("Rango:", format(startdate.tmp, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
total_deaths.others <- as.character(top20.total_deaths$location)[3:7]
plot.trend.total_deaths.month(startdate.tmp, enddate, total_deaths.others, title.tmp, sublabel.tmp)

-3 Semanas TOP[3:7]

sublabel.tmp <- paste("Rango:", format(enddate-21, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.total_deaths.monthdays(enddate-21, enddate, total_deaths.others, title.tmp, sublabel.tmp)

-3 Semanas IN, IR, MX, PE, RU

Las curvas de Francia, Italia y Reino Unido están planas, se ha estancado la cantidad de muertes atribuidas a COVID-19. Una posible razón es que la pandemia inició primero en Europa y la cantidad de muertes reportadas recientemente es nula o muy baja. Las curvas de México e India siguen en aumento. Se eliminan los países de Francia, Italia y Reino Unido y se agregan otros países en donde al parecer la cantidad de muertes atribuidas a COVID-19 aún sigue en aumento. Se genera la gráfica con las curvas de muertes acumuladas de las últimas 3 semanas de India, Iran, México, Perú y Rusia:

total_deaths.others <- as.character(top20.total_deaths$location)[c(3,5,8,9,10)]
countrieslabels <- paste(total_deaths.others, collapse = ', ')
title.tmp <- paste(title.tmp, "de", countrieslabels)
sublabel.tmp <- paste("Rango:", format(enddate-21, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.total_deaths.monthdays(enddate-21, enddate, total_deaths.others, title.tmp, sublabel.tmp)

-2 Semanas IN, IR, MX, PE, RU

total_deaths.others <- as.character(top20.total_deaths$location)[c(3,5,8,9,10)]
countrieslabels <- paste(total_deaths.others, collapse = ', ')
title.tmp <- paste(title.tmp, "de", countrieslabels)
sublabel.tmp <- paste("Rango:", format(enddate-14, "%d/%m/%Y"),"-", format(enddate, "%d/%m/%Y"))
plot.trend.total_deaths.monthdays(enddate-14, enddate, total_deaths.others, title.tmp, sublabel.tmp)

Comparación de curvas de contagios y muertes de México

Nuevos casos y nuevas muertes

covid.mexico <- covid.df[covid.df$location=='Mexico',]
covid.mexico <- covid.mexico[covid.mexico$date > "2020-04-01",]
covid.mexico.new <- covid.mexico[,c("date", "new_cases", "new_deaths")]
covid.mexico.new.lf <-melt(covid.mexico.new, id.vars = c("date"))
ggplot(data=covid.mexico.new.lf , aes(x=date, y=value, group=variable, colour=variable)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Indicador", labels=c("Nuevos Contagios", "Nuevas Muertes"))+
    scale_x_date(date_breaks = "week", date_labels = "%b %d")+
    ggtitle("Curvas de contagios y muertes diarias por COVID-19 en México")+
    labs(caption = plotscaption)+
    ylab("")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
          axis.text.y = element_text(size=11, face="bold", colour = "black"), 
          axis.text.x = element_text(size=11, face="bold", colour = "black"),
          legend.position = "bottom",
          legend.title = element_text(size = 14),
          legend.text = element_text(size = 13),
          legend.key.width = unit(1.5,"cm"))+
    guides(colour = guide_legend(override.aes = list(size=2)))

Nuevos casos y nuevas muertes último mes

covid.mexico.new.lastmonth <- covid.mexico.new[covid.mexico.new$date >= enddate-30, ]
covid.mexico.new.lastmonth.lf <- melt(covid.mexico.new.lastmonth, id.vars = c("date"))
ggplot(data=covid.mexico.new.lastmonth.lf , aes(x=date, y=value, group=variable, colour=variable)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Indicador", labels=c("Nuevos Contagios", "Nuevas Muertes"))+
    scale_x_date(date_breaks = "week", date_labels = "%b %d")+
    ggtitle("Curvas de contagios y muertes diarias por COVID-19 en México - Último mes")+
    labs(caption = plotscaption)+
    ylab("")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
          axis.text.y = element_text(size=11, face="bold", colour = "black"), 
          axis.text.x = element_text(size=11, face="bold", colour = "black"),
          legend.position = "bottom",
          legend.title = element_text(size = 14),
          legend.text = element_text(size = 13),
          legend.key.width = unit(1.5,"cm"))+
    guides(colour = guide_legend(override.aes = list(size=2)))

Contagios y muertes totales

covid.mexico.total <- covid.mexico[,c("date", "total_cases", "total_deaths")]
covid.mexico.total.lf <-melt(covid.mexico.total, id.vars = c("date"))
ggplot(data=covid.mexico.total.lf , aes(x=date, y=value, group=variable, colour=variable)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Indicador", labels=c("Contagios", "Muertes"))+
    scale_x_date(date_breaks = "week", date_labels = "%b %d")+
    scale_y_continuous(breaks=c(50000, 100000, 200000, 300000, 400000, 500000, 750000),
                     label=c("50k","100k", "200k", "300k", "400k", "500k", "750k"))+
    ggtitle("Curvas de contagios y muertes acumulados por COVID-19 en México")+
    labs(caption = plotscaption)+
    ylab("")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
          axis.text.y = element_text(size=11, face="bold", colour = "black"), 
          axis.text.x = element_text(size=11, face="bold", colour = "black"),
          legend.position = "bottom",
          legend.title = element_text(size = 14),
          legend.text = element_text(size = 13),
          legend.key.width = unit(1.5,"cm"))+
    guides(colour = guide_legend(override.aes = list(size=2)))

Contagios y muertes totales último mes

covid.mexico.total.lasttmonth <- covid.mexico.total[covid.mexico.total$date >= enddate-30, ]
covid.mexico.total.lasttmonth.lf <- melt(covid.mexico.total.lasttmonth, id.vars = c("date"))
ggplot(data=covid.mexico.total.lasttmonth.lf , aes(x=date, y=value, group=variable, colour=variable)) +
    geom_line(size=1) +
    scale_color_discrete(name = "Indicador", labels=c("Contagios", "Muertes"))+
    scale_x_date(date_breaks = "week", date_labels = "%b %d")+
    scale_y_continuous(breaks=c(50000, 100000, 200000, 300000, 400000, 500000, 750000),
                     label=c("50k","100k", "200k", "300k", "400k", "500k", "750k"))+
    ggtitle("Curvas de contagios y muertes acumulados por COVID-19 en México - Último mes")+
    labs(caption = plotscaption)+
    ylab("")+
    xlab("Fecha")+
    theme_bw()+
    theme(title = element_text(size=14, face="bold", colour = "black"),
          axis.text.y = element_text(size=11, face="bold", colour = "black"), 
          axis.text.x = element_text(size=11, face="bold", colour = "black"),
          legend.position = "bottom",
          legend.title = element_text(size = 14),
          legend.text = element_text(size = 13),
          legend.key.width = unit(1.5,"cm"))+
    guides(colour = guide_legend(override.aes = list(size=2)))

Casos y muertes COVID-19 semanales en México

covid.mexico <- covid.df[covid.df$location=='Mexico',]
week.agg <- covid.mexico %>%
  group_by(week = week(date)) %>%
  summarise(mean_new_cases = mean(new_cases),
            n = n(), 
            loCI_new_cases = ci(new_cases)[2], 
            hiCI_new_cases = ci(new_cases)[3], 
            mean_new_deaths = mean(new_deaths), 
            loCI_new_deaths = ci(new_deaths)[2], 
            hiCI_new_deaths = ci(new_deaths)[3], 
            total_new_cases = sum(new_cases), 
            total_new_deaths = sum(new_deaths))
# Weeks with zero mean new cases are deleted
week.agg <- week.agg[which(week.agg$mean_new_cases > 0),]
# Finding the date of the first week to be ploted
firstweek <- min(week.agg$week)
rowindex <- match(firstweek, week(covid.mexico[,"date"]))
firstweek.date <- format(covid.mexico[rowindex, ]$date, "%d/%m/%Y")
rm(firstweek, rowindex)
# Finding the date of the last week to be ploted
lastweek <- max(week.agg$week)
rowindex <- match(lastweek, week(covid.mexico[,"date"]))
lastweek.date <- format(covid.mexico[rowindex, ]$date, "%d/%m/%Y")
rm(lastweek, rowindex)
sublabel <- paste("Rango:", firstweek.date, "-", lastweek.date)

Promedio semanal de casos

ggplot(data=week.agg, aes(x=as.character(week),y=mean_new_cases))+
  geom_bar(stat = 'identity', aes(fill = mean_new_cases)) +
  geom_errorbar(aes(ymin=loCI_new_cases, ymax=hiCI_new_cases), width=.2,
                position=position_dodge(.9)) +
  xlab("Semana")+
  ylab("Promedio semanal de casos ± IC 95%")+
  labs(title="Promedio semanal de casos de COVID-19 en México",
       subtitle = sublabel,
       caption = plotscaption)+
  scale_fill_gradient(name="Casos", low = "green", high = "red")+
  theme_bw()+
  theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"))

Promedio semanal de muertes

ggplot(data=week.agg, aes(x=as.character(week),y=mean_new_deaths))+
  geom_bar(stat = 'identity', aes(fill = mean_new_deaths)) +
  geom_errorbar(aes(ymin=loCI_new_deaths, ymax=hiCI_new_deaths), width=.2,
                position=position_dodge(.9)) +
  xlab("Semana")+
  ylab("Promedio semanal de muertes ± IC 95%")+
  labs(title="Promedio semanal de muertes por COVID-19 en México",
       subtitle = sublabel,
       caption = plotscaption)+
  scale_fill_gradient(name="Muertes", low = "green", high = "red")+
  theme_bw()+
  theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"))

Casos totales por semana

ggplot(data=week.agg, aes(x=as.character(week),y=total_new_cases))+
  geom_bar(stat = 'identity', aes(fill = total_new_cases)) +
  xlab("Semana")+
  ylab("Casos totales por semana")+
  labs(title="Casos totales de COVID-19 por semana en México",
       subtitle = sublabel,
       caption = plotscaption)+
  scale_fill_gradient(name="Casos totales", low = "green", high = "red")+
  theme_bw()+
  theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"))

Muertes totales por semana

ggplot(data=week.agg, aes(x=as.character(week),y=total_new_deaths))+
  geom_bar(stat = 'identity', aes(fill = total_new_deaths)) +
  xlab("Semana")+
  ylab("Muertes totales por semana")+
  labs(title="Muertes totales por COVID-19 por semana en México",
       subtitle=sublabel,
       caption = plotscaption)+
  scale_fill_gradient(name="Muertes totales", low = "green", high = "red")+
  theme_bw()+
  theme(title = element_text(size=14, face="bold", colour = "black"),
        axis.text.y = element_text(size=11, face="bold", colour = "black"),
        axis.text.x = element_text(size=11, face="bold", colour = "black"))

Análisis Exploratorio de Datos (EDA) COVID-19

Oscar Castro

domingo, 16 de agosto de 2020

Descripción

Lectura de datos

La estructura del dataframe con las variables y su tipo:

Lista de países del conjunto de datos

Continentes del conjunto de datos

¿Cuál es la cantidad de días máximo que ha sido reportado por los países?

Países que han reportado datos la mayor cantidad de días

¿En qué fechas inician y terminan los datos reportados?

Rankings de países con las variables de totales o acumulados

Ranking de países: Total de casos reportados de COVID-19 con la columna total_cases

Tablas

Gráfica top 20

Ranking de países: Total de casos reportados de COVID-19 con la columna total_cases - Países de América

Tabla

Gráfica top 20

Ranking de países: Total de casos reportados de COVID-19 por millón de habitantes con la columna total_cases_per_million

Tablas

Gráfica top 20

Ranking de países: Total de muertes reportadas por COVID-19 con la columna total_deaths

Tablas

Gráfica top 20

Ranking de países: Total de muertes reportadas por COVID-19 por millón de habitantes con la columna total_deaths_per_million

Tabla

Gráfica top 20

Tendencias de contagios y muertes utilizando las columnas: new_cases y new_deaths

Nuevos contagios de los 5 países con más casos reportados de COVID-19 + México

Todo

-5 Meses

-3 Meses

-2 Meses

-1 Mes

-3 Semanas

-2 Semanas

Nuevas muertes de los 5 países con más casos reportados de COVID-19 + México

Todo

-5 Meses

-3 Meses

-2 Meses

-1 Mes

-3 Semanas

-2 Semanas

Acumulados de casos y muertes COVID-19

Curva de contagios acumulados de los 5 países con más casos reportados (+ México)

Todo

-5 Meses

-3 Meses

-2 Meses

-1 Mes

-1 Mes TOP[3:7]

-3 Semanas TOP[3:7]

-2 Semanas TOP[3:7]

-2 Semanas MX, PE y SA

Curvas de muertes acumuladas de los 5 países con más muertes reportadas (+ México)

Todo

-5 Meses

-3 Meses

-2 Meses

-1 Meses

-1 Mes TOP[3:7]

-3 Semanas TOP[3:7]

-3 Semanas IN, IR, MX, PE, RU

-2 Semanas IN, IR, MX, PE, RU

Comparación de curvas de contagios y muertes de México

Nuevos casos y nuevas muertes

Nuevos casos y nuevas muertes último mes

Contagios y muertes totales

Contagios y muertes totales último mes

Casos y muertes COVID-19 semanales en México

Promedio semanal de casos

Promedio semanal de muertes

Casos totales por semana

Muertes totales por semana